-The maximum amount of categories is actually 32767 not 32768, they vary from 1 to 32767. Category 0 is used to teach a counter example with the intend to correct neurons firing erroneously, but without committing a new neuron.-The input data can come from a variety of sources, this data is converted into pattern vectors which are then broadcasted to the neural network for either learning or recognition. These vectors are sequences of bytes with a length between 1 and 128 on Curie.-The amount of neurons used in different categories depend on the context that you train them. Usage of the context allows segmenting the network per family of input data creating sub-networks. This segmentation can be based on the model of the input sensor, the settings of the input sensor, the feature extracted from the sensor data, the data length, the time of collection of the data and more.-The free version of the library supports only a single context.

Those are facts taken from the documentation, now, regarding your question:

"...Assuming I have many vectors of say, 10 bytes, what is the maximum number of categories can I classify them under?..."

I believe that if you use vectors of 1 byte you will be able to use the maximum amount of categories but if you use vectors of 10 bytes you will have ten times less (~3276). However, I can't confirm this. So, I encourage you to check the documentation in order to learn more about it and if you have further questions regarding this library, I'd suggest you to contact General-Vision (http://www.general-vision.com/) as they developed this library and will be able to provide you a more accurate support.